C++ Coding - Black Scholes Option Pricing - Explicit Finite Difference

The example question for these solutions can be found on my website (click here).

6.1 Explicit Finite Difference For Option Pricing

In this example we are going to price a European call option with explicit finite difference.

C++ Coding - Black Scholes Option Pricing - Binomial Trees

The example question for these solutions can be found on my website (click here).

5.1 Binomial Tree For Option Pricing

The two most popular models for using binomial trees to price options are

• Cox et al. (1979) (CRR for short) whose extra degree of freedom is to set

  thus

  

• Rendleman and Bartter (1979) who choose:

  and so

  

We wish to generate a stock price tree, so denote the value of the underlying asset after timestep i and upstate j by S_ij and we have that:

C++ Coding - Black Scholes Option Pricing - Monte Carlo

3.2 Monte Carlo: Black Scholes European Call Option

Now we are going to value an European call option using Monte-Carlo. The setup is very simple, we just need to sum up the payoffs from a bunch of sample paths and then take the average. First start with an empty program except for the random number generator, as follows

Visual Studio 2012 at the University of Manchester

Getting started on Visual Studio 2012
A previous post of mine dealt with setting up an empty project on Visual Studio 2012, which you can find if you click here. If you follow the instructions there you will find that the terminal disappears even when you run the program with "Start Without Debugging". This is because some of the default properties are not set up correctly on opening an empty project.

C++ Coding - Time to first exit (C++11 VS2012)

4.1 Time To First Exit

Calculate the expected hitting time E[t^∗] for a Brownian motion X(t), where the process must hit either X(t^∗) = 0 or X(t^∗) = 1 for t^∗ < T. If neither boundary is hit within the time T then t^∗ = T.

Assume that the process X follows the SDE

where μ = 0.01 is the drift and σ = 0.75 is the standard deviation of the Brownian motion. We have that the initial start point of the Brownian motion is X(t = 0) = 0.56, and we set T = 1.

C++ Coding - Random Numbers (Update c++11 VS2012)

The example question for these solutions can be found on my website (click here).

2.1 Random Numbers

To use the new random number generator we need to include the random library, the cmath library for any calculations and also iostream to show results onscreen. We first create a new project with an empty program with the correct libraries, and then declare a variable of type mt19937. This declares a new random number generator, which generates pseudo random sequence of integers defined by the Mersenne Twister algorithm. A computer can only generate a random sequence of integers, but of course we can then take that sequence of integers and convert it to any required distribution. Some of the conversions are simple but others are more complex, luckily we now have inbuilt c++ conversion to all standard distributions (more on this later). This means you will always have to create a generator to pass as an argument to the probability distribution you want to generate.

C++ Examples - Data Types

Number Precision